PALM: PAnoramic Learning Map Integrating Learning Analytics and
Curriculum Map for Scalable Insights Across Courses

D 2025

PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses

OZAKI, Mahiro; Li CHEN; Shotaro NAGANUMA; Valdemar ŠVÁBENSKÝ; Fumiya OKUBO et. al.

Základní údaje

Originální název

PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses

Autoři

OZAKI, Mahiro; Li CHEN; Shotaro NAGANUMA; Valdemar ŠVÁBENSKÝ ; Fumiya OKUBO a Atsushi SHIMADA

Vydání

New York, NY, USA, Proceedings of the 2025 International Conference on Systems, Man, and Cybernetics [in press], 6 s. 2025

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Organizační jednotka

Fakulta informatiky

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 7. 2025 11:35, RNDr. Valdemar Švábenský, Ph.D.

Anotace

V originále

This study proposes and evaluates the PAnoramic Learning Map (PALM), a learning analytics (LA) dashboard designed to address the scalability challenges of LA by integrating curriculum-level information. Traditional LA research has predominantly focused on individual courses or learners and often lacks a framework that considers the relationships between courses and the long-term trajectory of learning. To bridge this gap, PALM was developed to integrate multilayered educational data into a curriculum map, enabling learners to intuitively understand their learning records and academic progression. We conducted a system evaluation to assess PALM’s effectiveness in two key areas: (1) its impact on students’ awareness of their learning behaviors, and (2) its comparative performance against existing systems. The results indicate that PALM enhances learners’ awareness of study planning and reflection, particularly by improving perceived behavioral control through the visual presentation of individual learning histories and statistical trends, which clarify the links between learning actions and outcomes. Although PALM requires ongoing refinement as a system, it received significantly higher evaluations than existing systems in terms of visual appeal and usability. By serving as an information resource with previously inaccessible insights, PALM enhances self-regulated learning and engagement, representing a significant step beyond conventional LA toward a comprehensive and scalable approach.

Citovat

OZAKI, Mahiro; Li CHEN; Shotaro NAGANUMA; Valdemar ŠVÁBENSKÝ; Fumiya OKUBO a Atsushi SHIMADA. PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses. Online. In Proceedings of the 2025 International Conference on Systems, Man, and Cybernetics [in press]. New York, NY, USA: IEEE, 2025, 6 s.

@inproceedings{2508746,
   author = {Ozaki, Mahiro and Chen, Li and Naganuma, Shotaro and Švábenský, Valdemar and Okubo, Fumiya and Shimada, Atsushi},
   address = {New York, NY, USA},
   booktitle = {Proceedings of the 2025 International Conference on Systems, Man, and Cybernetics [in press]},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   publisher = {IEEE},
   title = {PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses},
   year = {2025}
}

TY  - CONF
ID  - 2508746
AU  - Ozaki, Mahiro - Chen, Li - Naganuma, Shotaro - Švábenský, Valdemar - Okubo, Fumiya - Shimada, Atsushi
PY  - 2025
TI  - PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses
PB  - IEEE
CY  - New York, NY, USA
N2  - This study proposes and evaluates the PAnoramic Learning Map (PALM), a learning analytics (LA) dashboard designed to address the scalability challenges of LA by integrating curriculum-level information. Traditional LA research has predominantly focused on individual courses or learners and often lacks a framework that considers the relationships between courses and the long-term trajectory of learning. To bridge this gap, PALM was developed to integrate multilayered educational data into a curriculum map, enabling learners to intuitively understand their learning records and academic progression. We conducted a system evaluation to assess PALM’s effectiveness in two key areas: (1) its impact on students’ awareness of their learning behaviors, and (2) its comparative performance against existing systems. The results indicate that PALM enhances learners’ awareness of study planning and reflection, particularly by improving perceived behavioral control through the visual presentation of individual learning histories and statistical trends, which clarify the links between learning actions and outcomes. Although PALM requires ongoing refinement as a system, it received significantly higher evaluations than existing systems in terms of visual appeal and usability. By serving as an information resource with previously inaccessible insights, PALM enhances self-regulated learning and engagement, representing a significant step beyond conventional LA toward a comprehensive and scalable approach.
ER  -

OZAKI, Mahiro; Li CHEN; Shotaro NAGANUMA; Valdemar ŠVÁBENSKÝ; Fumiya OKUBO a Atsushi SHIMADA. PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses. Online. In \textit{Proceedings of the 2025 International Conference on Systems, Man, and Cybernetics [in press]}. New York, NY, USA: IEEE, 2025, 6 s.

Přehled o publikaci